Open Access
Issue |
JNWPU
Volume 40, Number 4, August 2022
|
|
---|---|---|
Page(s) | 944 - 952 | |
DOI | https://doi.org/10.1051/jnwpu/20224040944 | |
Published online | 30 September 2022 |
- XIAO Tong, LI Shuang, WANG Bochao, et al. Joint detection and identification feature learning for person search[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 3415–3424 [Google Scholar]
- WANG Zhongdao, ZHENG Liang, LIU Yixuan, et al. Towards real-time multi-object tracking[C]//Computer Vision-European Conference on Computer Vision, 2020: 107–122 [Google Scholar]
- ZHANG Yifu, WANG Chunyu, WANG Xinggang, et al. FairMOT: on the fairness of detection and re-identification in multiple object tracking[J]. International Journal of Computer Vision, 2021, 129(11): 3069–3087. [Article] [CrossRef] [Google Scholar]
- CHAABANE M, ZHANG P, BEVERIDGE J R, et al. DEFT: detection embeddings for tracking[EB/OL]. (2021-02-03)[2021-11-01]. [Article] [Google Scholar]
- GUO Song, WANG Jingya, WANG Xinchao, et al. Online multiple object tracking with cross-task synergy[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, 2021: 8132–8141 [Google Scholar]
- REDMON J, FARHADI A. Yolov3: An incremental improvement[EB/OL]. (2018-04-08)[2021-11-01]. [Article] [Google Scholar]
- KALMAN R E. A new approach to linear filtering and prediction problems[J]. Journal of Basic Engineering, 1960, 82: 35–45 [CrossRef] [Google Scholar]
- KUHN H W. The hungarian method for the assignment problem[J]. Naval Research Logistics Quarterly, 1955, 2(1/2): 83–97 [CrossRef] [Google Scholar]
- ZHANG Xuan, LUO Hao, FAN Xing, et al. AlignedReID: surpassing human-level performance in person re-identification[J/OL]. (2017-11-22)[2021-11-01]. [Article] [Google Scholar]
- GUO Jingda, MA Xu, SANSOM A, et al. Spanet: spatial pyramid attention network for enhanced image recognition[C]//2020 IEEE International Conference on Multimedia and Expo, London, 2020: 1–6 [Google Scholar]
- LIN T Y, DOLLAR P, GIRSHICK R. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017: 936–944 [CrossRef] [Google Scholar]
- HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Trans on Pattern Analysis & Machine Intelligence, 2014, 37(9): 1904–1916 [Google Scholar]
- DJORK-ARNÉ C, UNTERTHINER T, HOCHREITER S. Fast and accurate deep network learning by exponential linear units(ELUs)[C]//International Conference on Learning Representations, San Juan, Puerto Rico, 2016: 1–14 [Google Scholar]
- XIAO T, LI S, WANG B, et al. Joint detection and identification feature learning for person search[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 3415–3424 [Google Scholar]
- ZHENG L, ZHANG H, SUN S, et al. Person re-identification in the wild[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1367–1376 [Google Scholar]
- MILAN A, LEAL-TAIXE L, REID L, et al. Mot16: a benchmark for multi-object tracking[J/OL]. (2016-03-02)[2021-11-01]. [Article] [Google Scholar]
- LEAL-TAIXE L, MILAN A, REID I, et al, MOTChallenge 2015: towards a benchmark for multi-target tracking[EB/OL]. (2015-04-08)[2021-11-01]. [Article] [Google Scholar]
- DENDORFER P, REZATOFIGHI H, MILAN A, et al. MOT20: a benchmark for multi object tracking in crowded scenes[EB/OL]. (2020-03-19)[2021-11-01]. [Article] [Google Scholar]
- WOJKE N, BEWLEY A, PAULUS D. Simple online and realtime tracking with a deep association metric[C]//2017 IEEE International Conference on Image Processing, 2017: 3645–3649 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.